信阳师范学院学报（自然科学版）2024，Vol.37Issue(4)：477-484,8.DOI:10.3969/j.issn.1003-0972.2024.04.009
基于多尺度局部与全局上下文信息的钢材缺陷检测方法
Steel Surface Defect Detection Model Based on Multiscale Local and Global Context Information
摘要
Abstract
Steel surface defects have a significant impact on the quality and performance of many industrial products,which will bring huge economic losses to production.Therefore,it is very necessary to detect the steel surface in real time and find defects in time.In order to improve the detection performance of steel surface defects with large scale differences and complex backgrounds,a steel surface defect detection model based on multiscale local and global context information was proposed.Convolution operation with down-sampling mechanism in the convolutional neural network model was used to obtain rough multi-scale local feature maps.Then,self-attention mechanism was used to act on the local feature map extracted by convolution at each scale,to obtain long-distance dependencies between pixels(such as scratches,patches,inclusions,etc.),thus to enhance the inter-class discrimination ability of defects.Afterwards,the feature pyramid structure was used to fuse multi-scale feature maps,to improve the detection ability of multi-scale objects.Finally,channel and spatial attention module and WIoU loss function were introduced.The experimental results showed that,compared with algorithms such as Faster RCNN and EDDN,the proposed method was effective in improving the detection performance of steel surface defects.关键词
自注意力/表面缺陷检测/卷积神经网络/多尺度特征融合Key words
self-attention/surface defect detection/convolutional neural network(CNN)/multiscale feature fusion分类
信息技术与安全科学引用本文复制引用
张莉,付志鹏,郭华平,孙艳歌,李锡瑞..基于多尺度局部与全局上下文信息的钢材缺陷检测方法[J].信阳师范学院学报（自然科学版）,2024,37(4):477-484,8.基金项目
国家自然科学基金项目(62403405) （62403405）
河南省自然科学基金项目(222300420275) （222300420275）
河南省科技计划项目(242102210092) （242102210092）
河南省重点研发计划(241111212200) （241111212200）
河南省研究生教育优质课程项目(YJS2022KC34) （YJS2022KC34）
信阳师范学院"南湖学者奖励计划"青年项目 （）
